Abstract

Recent advances in text-to-image generation have improved the quality of synthesized images, but evaluations mainly focus on aesthetics or alignment with text prompts. Thus, it remains unclear whether these models can accurately represent a wide variety of realistic visual entities. To bridge this gap, we propose KITTEN, a benchmark for Knowledge-InTegrated image generaTion on real-world ENtities. Using KITTEN, we conduct a systematic study of recent text-to-image models, retrieval-augmented models, and unified understanding and generation models, focusing on their ability to generate real-world visual entities such as landmarks and animals. Analyses using carefully designed human evaluations, automatic metrics, and MLLMs as judges show that even advanced text-to-image and unified models fail to generate accurate visual details of entities. While retrieval-augmented models improve entity fidelity by incorporating reference images, they tend to over-rely on them and struggle to create novel configurations of the entities in creative text prompts. The dataset and evaluation code are publicly available at https://kitten-project.github.io.